Triple
T3292218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Dust |
E69126
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Guy Bensley |
E304842
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Guy Bensley | Statement: [Red Dust, editedBy, Guy Bensley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guy Bensley Context triple: [Red Dust, editedBy, Guy Bensley]
-
A.
Guy Bensley
chosen
Guy Bensley is a film editor best known for his work on the comedy spy film "Johnny English Reborn."
-
B.
Christopher Benstead
Christopher Benstead is a British composer and music editor known for his film scores and sound work on major movies, including collaborations with director Guy Ritchie.
-
C.
Andrew Bennison
Andrew Bennison was an American screenwriter active during the early sound era of Hollywood cinema.
-
D.
Sam Baldwin
Sam Baldwin is the widowed architect and devoted father portrayed by Tom Hanks in the romantic comedy film "Sleepless in Seattle."
-
E.
Max Dennison
Max Dennison is the skeptical teenage protagonist of the Halloween-themed fantasy film "Hocus Pocus," whose actions accidentally resurrect three witches in Salem.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb07379dc8190b7bb409bcf42bdd6 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b432ee11988190843e4b81500b65ca |
completed | March 13, 2026, 3:53 p.m. |
Created at: March 8, 2026, 3:10 p.m.